GEOPHYSICAL RESEARCH LETTERS VOL 35 L16709 doi1010292008GL035075 2008 Click Here

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Climate change hotspots in the United States

Noah S Diffenbaugh1 Filippo Giorgi2 and Jeremy S Pal3

Received 18 June 2008 revised 28 July 2008 accepted 31 July 2008 published 30 August 2008

[1] We use a multi-model multi-scenario climate model ensemble to identify climate change hotspots in the continental United States Our ensemble consists of the CMIP3 atmosphere-ocean general circulation models along with a high-resolution nested climate modeling system We test both high (A2) and low (B1) greenhouse gas emissions trajectories as well as two different statistical metrics for identifying regional climate change hotspots We find that the pattern of peak responsiveness in the CMIP3 ensemble is persistent across variations in GHG concentration GHG trajectory and identification method Areas of the southwestern United States and northern Mexico are the most persistent hotspots The high-resolution climate modeling system produces highly localized hotspots within the basic GCM structure but with a higher sensitivity to the identification method Across the ensemble the pattern of relative climate change hotspots is shaped primarily by changes in interannual variability of the contributing variables rather than by changes in the long-term means Citation Diffenbaugh N S F Giorgi and J S Pal

(2008) Climate change hotspots in the United States Geophys Res Lett 35 L16709 doi1010292008GL035075

1 Introduction

[2] Observed late 20th century global warming has been attributed primarily to anthropogenic changes in radiative forcing of the climate system [eg Intergovernmental Panel on Climate Change (IPCC) 2007] with further warming of approximately 1 to 6C likely to occur by the end of the 21st century [IPCC 2007] Precisely how this long-term global warming will manifest at smaller spatial and tempo-ral scales is a key question for understanding avoiding and or preparing for climate change In particular design and implementation of climate change mitigation and adaptation strategies requires quantification of potential spatial hetero-geneity in the aggregate climate response Therefore there exists a need to identify climate change hotspots that are likely to be most responsive to anthropogenic changes in climate forcing and to understand the mechanisms under-lying the enhanced responsiveness in the hotspot regions [3] Our analysis is focused on the continental United States

(US) The relative sensitivity of climate to greenhouse gas (GHG) forcing within the US is important for a number of reasons First the US encompasses a large continental area with a diversity of climatic regimes In addition it is home to a

1Purdue Climate Change Research Center and Department of Earth and Atmospheric Sciences Purdue University West Lafayette Indiana USA

2Abdus Salam International Centre for Theoretical Physics Trieste Italy

3Frank R Seaver College of Science and Engineering Loyola Marymount University Los Angeles California USA

Copyright 2008 by the American Geophysical Union 0094-8276082008GL035075$0500

substantial human population and a large and diverse econ-omy that is at least partly dependent on climate including a large fraction of the global agricultural production Further the US has become a significant party in climate change negotiations both because it is one of the largest GHG emitters and because local and state governments are entering into climate agreements independent of Federal action [eg Rabe 2004] The latter is particularly relevant for the identi-fication of climate change hotspots within the United States as the emergence of the state government as a primary unit of climate policy action enhances the need for regional- and state-level climate change information [Rabe 2004] [4] Climate change hotspots can be identified based on

the magnitude of physical climate response (as in work by Giorgi [2006]) or on the vulnerability to climate change impacts [eg Diffenbaugh et al 2007a] Specific impact assessments can provide detailed quantification of the poten-tial vulnerability of particular natural and human systems [eg White et al 2006] Although a framework does not yet exist for quantitatively exploring a large suite of possible impacts while also capturing the likely spatial complexity of physical climate change measures of the net change within multivariate climate space can serve as a metric of the total responsiveness of different geographic areas which can in turn provide a general indication of which areas might be faced with the greatest aggregate changes in physical climate stress in the coming decades To date such response-based hotspot identification has focused on global climate model assessment using either statistical [Williams et al 2007] or subjective [Diffenbaugh et al 2007a Giorgi 2006] aggre-gation of multiple climate variables [5] Here we focus on the physical response employing

statistical measures of aggregate climate change to identify regional climate change hotspots within the continental US We explore the climate change uncertainty space by ana-lyzing (1) the CMIP3 multi-model atmosphere-ocean gen-eral circulation model (AOGCM) ensemble [IPCC 2007] which allows us to examine the effect of different physical treatments of the climate system as well as different GHG emission scenarios (2) two aggregated statistical metrics of climate change which allows us to examine the sensitivity to the hotspot identification method and (3) a high-resolu-tion nested climate simulation which allows us to explore the importance of fine-scale processes in modulating the climate change signal The last of these is important because both the magnitude [eg Diffenbaugh et al 2005 2007] and impacts [eg White et al 2006] of simulated climate change can vary substantially at sub-GCM grid-scales

2 Models and Methods 21 Climate Model Data [6] We analyze AOGCM simulations from the CMIP3

multi-model archive The CMIP3 models successfully cap-

L16709 1 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

ture the structure of temperature and precipitation over the continental US [Randall et al 2007] In order to test the sensitivity of our hotspot identification to GHG concen-tration we analyze CMIP3 data from the A2 and B1 emission scenarios [IPCC 2000] The A2 and B1 socio-economic pathways result in GHG concentrations at the higher and lower ends of the IPCC illustrative scenario range respectively [IPCC 2007] In order to facilitate direct comparisons between the A2 and B1 ensemble simulations we use results from 15 models archiving data for both scenarios [7] To test the sensitivity of the hotspot identification

to fine-scale climate processes we employ the Abdus Salam International Center for Theoretical Physics Regional Climate Model (RegCM3) [Pal et al 2007] RegCM3 is able to capture the climatology of temperature and precipitation over a number regions of the world [eg Pal et al 2007] including the United States [eg Diffenbaugh et al 2006] We use the simulations described by Diffenbaugh et al [2005] White et al [2006] and Trapp et al [2007] The model domain covers the conterminous US and adjacent ocean waters with a 25 km horizontal grid interval and 18 levels in the vertical The RegCM3 simu-lations use atmospheric boundary conditions from the NASA finite volume GCM (FVGCM) simulations of Coppola and Giorgi [2005] In this configuration the global FVGCM grid has 1 horizontal resolution in latitude and 125 in longitude with 18 levels in the vertical In the reference simulation the models use observed monthly-varying SSTs while in the future climate simulation a monthly-varying SSTanomaly is added based on A2 scenario simulations with the HadCM3 AOGCM as described in [Giorgi et al 2004] Although the atmospheric boundary conditions provided by the FVGCM are equilibrated with the prescribed SSTs inconsistencies between the atmo-spheric and SST fields could be introduced by the lack of two-way coupling between the atmosphere and ocean in the FVGCM simulations [8] Finally in order to examine the role of the large-scale

boundary conditions in shaping the response of the high-resolution climate model we also analyze the FVGCM simulations using the reference period of 1961-1989 and the future period of 2071-2099 in the A2 scenario

22 Hotspot Identification [9] The goal of this study is to develop metrics for

comparing the relative responsiveness of different regions of the US within a multivariate climate space This hotspot identification requires aggregation of positive and negative changes in a number of climate variables of different scales and units In order to meet this challenge we use two statistical measures to identify climate change hotspots Our identification framework is based on that of Giorgi [2006] Diffenbaugh et al [2007a] and Williams et al [2007] Following Giorgi [2006] we quantify the aggregate response of mean and variability of seasonal temperature and precipitation We calculate mean temperature and precipitation as the long-term average of each yearrsquos seasonal mean Following Giorgi [2006] we calculate temperature variability as the interannual standard deviation of the seasonal means and precipitation variability as the interannual coefficient of variation of the seasonal

means (the interannual standard deviation divided by the long-term mean) after first detrending the seasonal timeseries [10] Following Giorgi [2006] Diffenbaugh et al

[2007a] and Williams et al [2007] we separate each year into two seasons in order to capture sub-annual changes that could potentially cancel at the annual-scale (for instance drying in one season and wetting in another) We follow Giorgi [2006] and Diffenbaugh et al [2007a] in designating April through September and October through March as the two 6-month seasons for the continental US These also encompass the June-July-August and December-January-February seasons of Williams et al [2007] We aggregate the different seasons by treating them as different variables yielding eight total variables to be used in our aggregation metrics (long-term mean and variability of warm- and cold-season temperature and precipitation) [11] We employ two hotspot identification metrics The

first follows Williams et al [2007] in employing the Standard Euclidean Distance (SED) to measure the distance traveled in multivariate climate space At each land grid point we calculate the total SED between the future (f) and present (p) periods as

SEDfp frac14 sqrtethSvSEDvTHORN eth1THORN

for

2 2 SEDv frac14 xfv xpv = mean abs xfv xpv eth2THORN

ij

where xfv is the value of variable v in the future period xpv isthe value of variable v in the reference period and mean[abs(xfv ndash xpv)]ij is the mean of the absolute value of land-grid-point change for variable v over all land grid points ij The denominator in equation (2) is used to normalize the metric We find that using a regional or global domain in the denominator yields similar results (not shown) [12] As an additional metric we use the squared cord

distance dissimilarity coefficient (SCD) of Overpeck et al [1992] The SCD quantifies the dissimilarity between sam-ple populations At each land grid point we calculate the total SCD between the future (f) and present (p) periods as the sum of the SCD scores for each variable v

SCDfp frac14 ethSvSCDvTHORN eth3THORN

for

2 21=2 1=2

SCDv frac14 =x 1 2fv x =SD x x 1=2pv fv pv eth4THORN

ij

where 12 SD([(x 12fv ndash x 2

pv ) ])ij is the standard deviation of the land-grid-point dissimilarity values for variable v over all land grid points ij Because we normalize the SED values by the grid-point mean we instead normalize the SCD values by the grid-point standard deviation in order to explore the sensitivity of the hotspot identification to the details of the identification method

2 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

Figure 1 Aggregate climate change scores in the United States (top) aggregate scores using the Standard Euclidean Distance (SED) and (bottom) aggregate scores using the Squared Cord Distance (SCD) Unitless

[13] In aggregating the different models in the CMIP3 ensemble we first calculate the eight variables for each model individually then calculate the mean value of each variable from all of the models and then calculate the aggregate metrics using those multi-model mean values of the eight variables

3 Results

[14] The CMIP3 ensemble dataset shows peak SED scores (ie hotspots) over southern California northern Mexico and western Texas (Figure 1) Minimum CMIP3 SED scores occur over the Gulf Coast and Atlantic Coast regions as well as the northern Great Plains The FVGCM dataset shows SED peaks over central California northern Mexico and western Texas although these are more local-ized than in the CMIP3 dataset In addition the FVGCM shows relatively high SED scores over the Midwestern US

and minimum SED scores over the southeastern and north-western US The RegCM3 dataset shows highly localized peak SED scores over southern California western Arizona northern Mexico and the Atlantic Coast along with sec-ondary peaks over the Midwestern US The SCD scores show generally similar patterns as the SED scores for all three datasets Key exceptions include muting of the central California hotspot in the FVGCM simulations and en-hancement of the Midwestern hotspot in all three datasets [15] The pattern of SED hotspots is shaped more by

changes in interannual variability than by changes in the long-term mean of the contributing variables (Figure 2) For instance the two most prominent contributors to the CMIP3 southern California hotspot are the two seasonal precipita-tion variability variables and the three most prominent contributors to the CMIP3 northern Mexico and western Texas hotspots are the two seasonal precipitation variability variables and October-March temperature variability (along

Figure 2 Variables contributing to aggregate climate change hotspots in the United States (top) variables contributing to the CMIP3 hotspots (middle) variables contributing to the FVGCM hotspots and (bottom) contributing to the RegCM3 hotspots The four variables making the highest contribution to the Standard Euclidean Distance (SED) hotspots identified in Figure 1 are shown for each of the climate model datasets In order to most readily compare the variable contributions with the net SED value we show the square root of the individual SEDv variable scores (see equation (2)) Unitless

3 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

Figure 3 Sensitivity of aggregate climate change scores to greenhouse gas concentration and pathway (top) results for the A2 emissions scenario and (bottom) results for the B1 emissions scenario The aggregate Standard Euclidean Distance (SED) scores are shown for three three-decade periods of the 21st century Unitless

with April-September mean precipitation over northern Texas) Likewise the primary contributors to the FVGCM central California and western Texas hotspots are the two seasonal precipitation variability variables and the two most prominent contributors to the FVGCM SED Midwestern hotspot are the two seasonal temperature variability variables Additionally the primary contributors to the RegCM3 southern California and western Arizona SED hotpots are the two precipitation variability variables Across the simulations warm-season variability is generally a stronger contributor than cold-season variability (Figure 2) [16] The pattern of SED hotspots shows little sensitivity

to either the time evolution or the total concentration of atmospheric GHGs (Figure 3) For instance in both the A2 and B1 emissions scenarios southern California western Texas and northern Mexico emerge with the highest SED scores in the late 21st century with the southeastern US and northern Great Plains showing the lowest SED scores Likewise this is the basic hotspot pattern in both scenarios throughout the 21st century Notable exceptions include a narrowing of the southern California hotspot in the early 21st century of the A2 CMIP3 dataset as well as a slight westward migration of the Texas hotspot in the late 21st century of the A2 CMIP3 dataset

4 Discussion and Conclusions

[17] The pattern of climate change hotspots in the United States is generally persistent in the CMIP3 multi-AOGCM ensemble Southern California northern Mexico and west-ern Texas show the greatest climate change responsiveness in the late-21st century using either of the identification methods that we have applied The Midwest region also shows relatively high hotspot metric values while the Gulf Coast and Atlantic Coast regions show the least respon-siveness (Figure 1) Similarly the pattern of responsiveness is largely consistent between high- and low-end emissions scenarios and throughout the 21st century (Figure 3) This pattern is mostly in place even in the early 21st century when differences in GHG forcing are relatively small compared to present and the inter-model variability could damp the climate change signal This persistence of the hotspot pattern in the CMIP3 ensemble suggests that the broad patterns of climate responsiveness may be robust to climate system variability (Whether the patterns are robust to model formulation requires further analysis)

[18] It is also notable that a single FVGCM realization not included in the CMIP3 ensemble displays a number of features seen in that ensemble (particularly for the SED metric Figure 1) FVGCM does show considerably more sensitivity to the hotspot identification method (as does RegCM3) particularly over California and the Midwestern United States This sensitivity of the single realization to the identification method could indicate that multiple realiza-tions using multiple climate models are required in order to generate reliable projections of relative regional climate sensitivity In addition the fact that the FVGCM dataset consists of only one realization could explain the strength of the Midwestern hotspot in the FVGCM dataset relative to the CMIP3 dataset with multiple realizations in the CMIP3 ensemble potentially damping the magnitude of changes in seasonal temperature variability (Figure 2) Alternatively the relatively fine horizontal resolution of the FVGCM grid could also contribute to deviations from the CMIP3 ensem-ble Likewise whereas the CMIP3 ensemble consists of coupled atmosphere-ocean GCM simulations the FVGCM realization was generated using prescribed SSTs synthesized by adding simulated SST changes to an observational SST timeseries (as described by Giorgi et al [2004] [19] The pattern of SED and SCD scores in the RegCM3

simulations highlights the limitations of applying high-resolution climate models to statistical climate change hotspot identification Although the RegCM3 simulations reveal high scores within some of the areas identified in the GCM simulations those hotspot areas are highly localized with considerably lower scores elsewhere in the domain (Figure 1) This localization is also seen in the contributing variables with a large range between the few grid-points showing maximum values and the vast majority of grid-points showing minimum values (Figure 2) Some of the differences between the RegCM3 and CMIP3 patterns can be linked to the forcing by the FVGCM (such as the relatively high SCD and SED values over the Midwestern US) However the overall pattern in the RegCM3 simu-lations deviates more from the driving FVGCM than the FVGCM deviates from the CMIP3 ensemble highlighting the need for multiple realizations with multiple GCM-RCM combinations in order to generate robust high-resolution hotspot identification [20] Peak aggregate climate changes in all three climate

model datasets are driven primarily by changes in interan-nual variability particularly of precipitation This domi-nance of variability results from the fact that there is more

4 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

inter-grid-point heterogeneity in the changes in variability than in the changes in seasonal mean The fact that the hotspot distribution is strongly influenced by changes in precipitation variability highlights the importance of accurate modeling of cloud and precipitation processes Further non-hotspot areas should not be considered to be immune from climate change as the climatic changes projected here could have substantial impact in areas that are not identified as response hotspots [eg Trapp et al 2007 White et al 2006]

[21] Acknowledgments We thank two anonymous reviewers for insightful and constructive comments The RegCM3 simulations were generated using computational resources in the Rosen Center for Advanced Computing at Purdue University This work was supported in part by NSF awards 0450221 and 0315677

References Coppola E and F Giorgi (2005) Climate change in tropical regions from high-resolution time-slice AGCM experiments Q J R Meteorol Soc 131 3123 ndash3145

Diffenbaugh N S et al (2005) Fine-scale processes regulate the response of extreme events to global climate change Proc Natl Acad Sci U S A 102 15774ndash15778

Diffenbaugh N S M Ashfaq B Shuman J W Williams and P J Bartlein (2006) Summer aridity in the United States Response to mid-Holocene changes in insolation and sea surface temperature Geophys Res Lett 33 L22712 doi1010292006GL028012

Diffenbaugh N S et al (2007a) Indicators of 21st century socioclimatic exposure Proc Natl Acad Sci U S A 104 20195 ndash20198

Diffenbaugh N S J S Pal F Giorgi and X Gao (2007b) Heat stress intensification in the Mediterranean climate change hotspot Geophys Res Lett 34 L11706 doi1010292007GL030000

Giorgi F (2006) Climate change hot-spots Geophys Res Lett 33 L08707 doi1010292006GL025734

Giorgi F (2004) Mean interannual variability and trends in a regional climate change experiment over Europe II Climate change scenarios (2071ndash2100) Clim Dyn 23 839 ndash858

Intergovernmental Panel on Climate Change (IPCC) (2000) Special Report on Emissions Scenarios edited by N Nakiaeligenoviaelig 570 pp Cambridge Univ Press Cambridge U K

Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al Cambridge Univ Press Cambridge U K

Overpeck J T et al (1992) Mapping eastern North American vegetation change of the past 18 ka No-analogs and the future Geology 20 1071ndash1074

Pal J S et al (2007) Regional climate modeling for the developing world The ICTP RegCM3 and RegCNET Bull Am Meteorol Soc 89 1395 ndash1409

Rabe B (2004) Statehouse and Greenhouse Brookings Inst Washington D C

Randall D A et al (2007) Climate models and their evaluation in Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al pp 589 ndash662 Cambridge Univ Press Cambridge U K

Trapp R J et al (2007) Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing Proc Natl Acad Sci U S A 104 19719 ndash 19723

White M A et al (2006) Extreme heat reduces and shifts United States premium wine production in the 21st century Proc Natl Acad Sci U S A 103 11217 ndash11222

Williams J W et al (2007) Projected distributions of novel and disappearing climates by 2100AD Proc Natl Acad Sci U S A 104 5738ndash5742

N S Diffenbaugh Purdue Climate Change Research Center Purdue University 550 Stadium Mall Drive West Lafayette IN 47907-2051 USA (diffenbaughpurdueedu) F Giorgi Abdus Salam International Centre for Theoretical Physics

Strada Costiera 11 I-34014 Trieste Italy J S Pal Frank R Seaver College of Science and Engineering Loyola

Marymount University 1 LMU Drive MS 8135 Los Angeles CA 90045 USA

5 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

Figure 1 Aggregate climate change scores in the United States (top) aggregate scores using the Standard Euclidean Distance (SED) and (bottom) aggregate scores using the Squared Cord Distance (SCD) Unitless

[13] In aggregating the different models in the CMIP3 ensemble we first calculate the eight variables for each model individually then calculate the mean value of each variable from all of the models and then calculate the aggregate metrics using those multi-model mean values of the eight variables

3 Results

[14] The CMIP3 ensemble dataset shows peak SED scores (ie hotspots) over southern California northern Mexico and western Texas (Figure 1) Minimum CMIP3 SED scores occur over the Gulf Coast and Atlantic Coast regions as well as the northern Great Plains The FVGCM dataset shows SED peaks over central California northern Mexico and western Texas although these are more local-ized than in the CMIP3 dataset In addition the FVGCM shows relatively high SED scores over the Midwestern US

and minimum SED scores over the southeastern and north-western US The RegCM3 dataset shows highly localized peak SED scores over southern California western Arizona northern Mexico and the Atlantic Coast along with sec-ondary peaks over the Midwestern US The SCD scores show generally similar patterns as the SED scores for all three datasets Key exceptions include muting of the central California hotspot in the FVGCM simulations and en-hancement of the Midwestern hotspot in all three datasets [15] The pattern of SED hotspots is shaped more by

changes in interannual variability than by changes in the long-term mean of the contributing variables (Figure 2) For instance the two most prominent contributors to the CMIP3 southern California hotspot are the two seasonal precipita-tion variability variables and the three most prominent contributors to the CMIP3 northern Mexico and western Texas hotspots are the two seasonal precipitation variability variables and October-March temperature variability (along

Figure 2 Variables contributing to aggregate climate change hotspots in the United States (top) variables contributing to the CMIP3 hotspots (middle) variables contributing to the FVGCM hotspots and (bottom) contributing to the RegCM3 hotspots The four variables making the highest contribution to the Standard Euclidean Distance (SED) hotspots identified in Figure 1 are shown for each of the climate model datasets In order to most readily compare the variable contributions with the net SED value we show the square root of the individual SEDv variable scores (see equation (2)) Unitless

3 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

Figure 3 Sensitivity of aggregate climate change scores to greenhouse gas concentration and pathway (top) results for the A2 emissions scenario and (bottom) results for the B1 emissions scenario The aggregate Standard Euclidean Distance (SED) scores are shown for three three-decade periods of the 21st century Unitless

with April-September mean precipitation over northern Texas) Likewise the primary contributors to the FVGCM central California and western Texas hotspots are the two seasonal precipitation variability variables and the two most prominent contributors to the FVGCM SED Midwestern hotspot are the two seasonal temperature variability variables Additionally the primary contributors to the RegCM3 southern California and western Arizona SED hotpots are the two precipitation variability variables Across the simulations warm-season variability is generally a stronger contributor than cold-season variability (Figure 2) [16] The pattern of SED hotspots shows little sensitivity

to either the time evolution or the total concentration of atmospheric GHGs (Figure 3) For instance in both the A2 and B1 emissions scenarios southern California western Texas and northern Mexico emerge with the highest SED scores in the late 21st century with the southeastern US and northern Great Plains showing the lowest SED scores Likewise this is the basic hotspot pattern in both scenarios throughout the 21st century Notable exceptions include a narrowing of the southern California hotspot in the early 21st century of the A2 CMIP3 dataset as well as a slight westward migration of the Texas hotspot in the late 21st century of the A2 CMIP3 dataset

4 Discussion and Conclusions

[17] The pattern of climate change hotspots in the United States is generally persistent in the CMIP3 multi-AOGCM ensemble Southern California northern Mexico and west-ern Texas show the greatest climate change responsiveness in the late-21st century using either of the identification methods that we have applied The Midwest region also shows relatively high hotspot metric values while the Gulf Coast and Atlantic Coast regions show the least respon-siveness (Figure 1) Similarly the pattern of responsiveness is largely consistent between high- and low-end emissions scenarios and throughout the 21st century (Figure 3) This pattern is mostly in place even in the early 21st century when differences in GHG forcing are relatively small compared to present and the inter-model variability could damp the climate change signal This persistence of the hotspot pattern in the CMIP3 ensemble suggests that the broad patterns of climate responsiveness may be robust to climate system variability (Whether the patterns are robust to model formulation requires further analysis)

[18] It is also notable that a single FVGCM realization not included in the CMIP3 ensemble displays a number of features seen in that ensemble (particularly for the SED metric Figure 1) FVGCM does show considerably more sensitivity to the hotspot identification method (as does RegCM3) particularly over California and the Midwestern United States This sensitivity of the single realization to the identification method could indicate that multiple realiza-tions using multiple climate models are required in order to generate reliable projections of relative regional climate sensitivity In addition the fact that the FVGCM dataset consists of only one realization could explain the strength of the Midwestern hotspot in the FVGCM dataset relative to the CMIP3 dataset with multiple realizations in the CMIP3 ensemble potentially damping the magnitude of changes in seasonal temperature variability (Figure 2) Alternatively the relatively fine horizontal resolution of the FVGCM grid could also contribute to deviations from the CMIP3 ensem-ble Likewise whereas the CMIP3 ensemble consists of coupled atmosphere-ocean GCM simulations the FVGCM realization was generated using prescribed SSTs synthesized by adding simulated SST changes to an observational SST timeseries (as described by Giorgi et al [2004] [19] The pattern of SED and SCD scores in the RegCM3

simulations highlights the limitations of applying high-resolution climate models to statistical climate change hotspot identification Although the RegCM3 simulations reveal high scores within some of the areas identified in the GCM simulations those hotspot areas are highly localized with considerably lower scores elsewhere in the domain (Figure 1) This localization is also seen in the contributing variables with a large range between the few grid-points showing maximum values and the vast majority of grid-points showing minimum values (Figure 2) Some of the differences between the RegCM3 and CMIP3 patterns can be linked to the forcing by the FVGCM (such as the relatively high SCD and SED values over the Midwestern US) However the overall pattern in the RegCM3 simu-lations deviates more from the driving FVGCM than the FVGCM deviates from the CMIP3 ensemble highlighting the need for multiple realizations with multiple GCM-RCM combinations in order to generate robust high-resolution hotspot identification [20] Peak aggregate climate changes in all three climate

model datasets are driven primarily by changes in interan-nual variability particularly of precipitation This domi-nance of variability results from the fact that there is more

4 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

inter-grid-point heterogeneity in the changes in variability than in the changes in seasonal mean The fact that the hotspot distribution is strongly influenced by changes in precipitation variability highlights the importance of accurate modeling of cloud and precipitation processes Further non-hotspot areas should not be considered to be immune from climate change as the climatic changes projected here could have substantial impact in areas that are not identified as response hotspots [eg Trapp et al 2007 White et al 2006]

[21] Acknowledgments We thank two anonymous reviewers for insightful and constructive comments The RegCM3 simulations were generated using computational resources in the Rosen Center for Advanced Computing at Purdue University This work was supported in part by NSF awards 0450221 and 0315677

References Coppola E and F Giorgi (2005) Climate change in tropical regions from high-resolution time-slice AGCM experiments Q J R Meteorol Soc 131 3123 ndash3145

Diffenbaugh N S et al (2005) Fine-scale processes regulate the response of extreme events to global climate change Proc Natl Acad Sci U S A 102 15774ndash15778

Diffenbaugh N S M Ashfaq B Shuman J W Williams and P J Bartlein (2006) Summer aridity in the United States Response to mid-Holocene changes in insolation and sea surface temperature Geophys Res Lett 33 L22712 doi1010292006GL028012

Diffenbaugh N S et al (2007a) Indicators of 21st century socioclimatic exposure Proc Natl Acad Sci U S A 104 20195 ndash20198

Diffenbaugh N S J S Pal F Giorgi and X Gao (2007b) Heat stress intensification in the Mediterranean climate change hotspot Geophys Res Lett 34 L11706 doi1010292007GL030000

Giorgi F (2006) Climate change hot-spots Geophys Res Lett 33 L08707 doi1010292006GL025734

Giorgi F (2004) Mean interannual variability and trends in a regional climate change experiment over Europe II Climate change scenarios (2071ndash2100) Clim Dyn 23 839 ndash858

Intergovernmental Panel on Climate Change (IPCC) (2000) Special Report on Emissions Scenarios edited by N Nakiaeligenoviaelig 570 pp Cambridge Univ Press Cambridge U K

Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al Cambridge Univ Press Cambridge U K

Overpeck J T et al (1992) Mapping eastern North American vegetation change of the past 18 ka No-analogs and the future Geology 20 1071ndash1074

Pal J S et al (2007) Regional climate modeling for the developing world The ICTP RegCM3 and RegCNET Bull Am Meteorol Soc 89 1395 ndash1409

Rabe B (2004) Statehouse and Greenhouse Brookings Inst Washington D C

Randall D A et al (2007) Climate models and their evaluation in Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al pp 589 ndash662 Cambridge Univ Press Cambridge U K

Trapp R J et al (2007) Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing Proc Natl Acad Sci U S A 104 19719 ndash 19723

White M A et al (2006) Extreme heat reduces and shifts United States premium wine production in the 21st century Proc Natl Acad Sci U S A 103 11217 ndash11222

Williams J W et al (2007) Projected distributions of novel and disappearing climates by 2100AD Proc Natl Acad Sci U S A 104 5738ndash5742

N S Diffenbaugh Purdue Climate Change Research Center Purdue University 550 Stadium Mall Drive West Lafayette IN 47907-2051 USA (diffenbaughpurdueedu) F Giorgi Abdus Salam International Centre for Theoretical Physics

Strada Costiera 11 I-34014 Trieste Italy J S Pal Frank R Seaver College of Science and Engineering Loyola

Marymount University 1 LMU Drive MS 8135 Los Angeles CA 90045 USA

5 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

Figure 3 Sensitivity of aggregate climate change scores to greenhouse gas concentration and pathway (top) results for the A2 emissions scenario and (bottom) results for the B1 emissions scenario The aggregate Standard Euclidean Distance (SED) scores are shown for three three-decade periods of the 21st century Unitless

with April-September mean precipitation over northern Texas) Likewise the primary contributors to the FVGCM central California and western Texas hotspots are the two seasonal precipitation variability variables and the two most prominent contributors to the FVGCM SED Midwestern hotspot are the two seasonal temperature variability variables Additionally the primary contributors to the RegCM3 southern California and western Arizona SED hotpots are the two precipitation variability variables Across the simulations warm-season variability is generally a stronger contributor than cold-season variability (Figure 2) [16] The pattern of SED hotspots shows little sensitivity

to either the time evolution or the total concentration of atmospheric GHGs (Figure 3) For instance in both the A2 and B1 emissions scenarios southern California western Texas and northern Mexico emerge with the highest SED scores in the late 21st century with the southeastern US and northern Great Plains showing the lowest SED scores Likewise this is the basic hotspot pattern in both scenarios throughout the 21st century Notable exceptions include a narrowing of the southern California hotspot in the early 21st century of the A2 CMIP3 dataset as well as a slight westward migration of the Texas hotspot in the late 21st century of the A2 CMIP3 dataset

4 Discussion and Conclusions

[17] The pattern of climate change hotspots in the United States is generally persistent in the CMIP3 multi-AOGCM ensemble Southern California northern Mexico and west-ern Texas show the greatest climate change responsiveness in the late-21st century using either of the identification methods that we have applied The Midwest region also shows relatively high hotspot metric values while the Gulf Coast and Atlantic Coast regions show the least respon-siveness (Figure 1) Similarly the pattern of responsiveness is largely consistent between high- and low-end emissions scenarios and throughout the 21st century (Figure 3) This pattern is mostly in place even in the early 21st century when differences in GHG forcing are relatively small compared to present and the inter-model variability could damp the climate change signal This persistence of the hotspot pattern in the CMIP3 ensemble suggests that the broad patterns of climate responsiveness may be robust to climate system variability (Whether the patterns are robust to model formulation requires further analysis)

[18] It is also notable that a single FVGCM realization not included in the CMIP3 ensemble displays a number of features seen in that ensemble (particularly for the SED metric Figure 1) FVGCM does show considerably more sensitivity to the hotspot identification method (as does RegCM3) particularly over California and the Midwestern United States This sensitivity of the single realization to the identification method could indicate that multiple realiza-tions using multiple climate models are required in order to generate reliable projections of relative regional climate sensitivity In addition the fact that the FVGCM dataset consists of only one realization could explain the strength of the Midwestern hotspot in the FVGCM dataset relative to the CMIP3 dataset with multiple realizations in the CMIP3 ensemble potentially damping the magnitude of changes in seasonal temperature variability (Figure 2) Alternatively the relatively fine horizontal resolution of the FVGCM grid could also contribute to deviations from the CMIP3 ensem-ble Likewise whereas the CMIP3 ensemble consists of coupled atmosphere-ocean GCM simulations the FVGCM realization was generated using prescribed SSTs synthesized by adding simulated SST changes to an observational SST timeseries (as described by Giorgi et al [2004] [19] The pattern of SED and SCD scores in the RegCM3

simulations highlights the limitations of applying high-resolution climate models to statistical climate change hotspot identification Although the RegCM3 simulations reveal high scores within some of the areas identified in the GCM simulations those hotspot areas are highly localized with considerably lower scores elsewhere in the domain (Figure 1) This localization is also seen in the contributing variables with a large range between the few grid-points showing maximum values and the vast majority of grid-points showing minimum values (Figure 2) Some of the differences between the RegCM3 and CMIP3 patterns can be linked to the forcing by the FVGCM (such as the relatively high SCD and SED values over the Midwestern US) However the overall pattern in the RegCM3 simu-lations deviates more from the driving FVGCM than the FVGCM deviates from the CMIP3 ensemble highlighting the need for multiple realizations with multiple GCM-RCM combinations in order to generate robust high-resolution hotspot identification [20] Peak aggregate climate changes in all three climate

model datasets are driven primarily by changes in interan-nual variability particularly of precipitation This domi-nance of variability results from the fact that there is more

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L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

inter-grid-point heterogeneity in the changes in variability than in the changes in seasonal mean The fact that the hotspot distribution is strongly influenced by changes in precipitation variability highlights the importance of accurate modeling of cloud and precipitation processes Further non-hotspot areas should not be considered to be immune from climate change as the climatic changes projected here could have substantial impact in areas that are not identified as response hotspots [eg Trapp et al 2007 White et al 2006]

[21] Acknowledgments We thank two anonymous reviewers for insightful and constructive comments The RegCM3 simulations were generated using computational resources in the Rosen Center for Advanced Computing at Purdue University This work was supported in part by NSF awards 0450221 and 0315677

References Coppola E and F Giorgi (2005) Climate change in tropical regions from high-resolution time-slice AGCM experiments Q J R Meteorol Soc 131 3123 ndash3145

Diffenbaugh N S et al (2005) Fine-scale processes regulate the response of extreme events to global climate change Proc Natl Acad Sci U S A 102 15774ndash15778

Diffenbaugh N S M Ashfaq B Shuman J W Williams and P J Bartlein (2006) Summer aridity in the United States Response to mid-Holocene changes in insolation and sea surface temperature Geophys Res Lett 33 L22712 doi1010292006GL028012

Diffenbaugh N S et al (2007a) Indicators of 21st century socioclimatic exposure Proc Natl Acad Sci U S A 104 20195 ndash20198

Diffenbaugh N S J S Pal F Giorgi and X Gao (2007b) Heat stress intensification in the Mediterranean climate change hotspot Geophys Res Lett 34 L11706 doi1010292007GL030000

Giorgi F (2006) Climate change hot-spots Geophys Res Lett 33 L08707 doi1010292006GL025734

Giorgi F (2004) Mean interannual variability and trends in a regional climate change experiment over Europe II Climate change scenarios (2071ndash2100) Clim Dyn 23 839 ndash858

Intergovernmental Panel on Climate Change (IPCC) (2000) Special Report on Emissions Scenarios edited by N Nakiaeligenoviaelig 570 pp Cambridge Univ Press Cambridge U K

Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al Cambridge Univ Press Cambridge U K

Overpeck J T et al (1992) Mapping eastern North American vegetation change of the past 18 ka No-analogs and the future Geology 20 1071ndash1074

Pal J S et al (2007) Regional climate modeling for the developing world The ICTP RegCM3 and RegCNET Bull Am Meteorol Soc 89 1395 ndash1409

Rabe B (2004) Statehouse and Greenhouse Brookings Inst Washington D C

Randall D A et al (2007) Climate models and their evaluation in Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al pp 589 ndash662 Cambridge Univ Press Cambridge U K

Trapp R J et al (2007) Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing Proc Natl Acad Sci U S A 104 19719 ndash 19723

White M A et al (2006) Extreme heat reduces and shifts United States premium wine production in the 21st century Proc Natl Acad Sci U S A 103 11217 ndash11222

Williams J W et al (2007) Projected distributions of novel and disappearing climates by 2100AD Proc Natl Acad Sci U S A 104 5738ndash5742

N S Diffenbaugh Purdue Climate Change Research Center Purdue University 550 Stadium Mall Drive West Lafayette IN 47907-2051 USA (diffenbaughpurdueedu) F Giorgi Abdus Salam International Centre for Theoretical Physics

Strada Costiera 11 I-34014 Trieste Italy J S Pal Frank R Seaver College of Science and Engineering Loyola

Marymount University 1 LMU Drive MS 8135 Los Angeles CA 90045 USA

5 of 5

L16709 DIFFENBAUGH ET AL CLIMATE CHANGE HOTSPOTS L16709

inter-grid-point heterogeneity in the changes in variability than in the changes in seasonal mean The fact that the hotspot distribution is strongly influenced by changes in precipitation variability highlights the importance of accurate modeling of cloud and precipitation processes Further non-hotspot areas should not be considered to be immune from climate change as the climatic changes projected here could have substantial impact in areas that are not identified as response hotspots [eg Trapp et al 2007 White et al 2006]

[21] Acknowledgments We thank two anonymous reviewers for insightful and constructive comments The RegCM3 simulations were generated using computational resources in the Rosen Center for Advanced Computing at Purdue University This work was supported in part by NSF awards 0450221 and 0315677

References Coppola E and F Giorgi (2005) Climate change in tropical regions from high-resolution time-slice AGCM experiments Q J R Meteorol Soc 131 3123 ndash3145

Diffenbaugh N S et al (2005) Fine-scale processes regulate the response of extreme events to global climate change Proc Natl Acad Sci U S A 102 15774ndash15778

Diffenbaugh N S M Ashfaq B Shuman J W Williams and P J Bartlein (2006) Summer aridity in the United States Response to mid-Holocene changes in insolation and sea surface temperature Geophys Res Lett 33 L22712 doi1010292006GL028012

Diffenbaugh N S et al (2007a) Indicators of 21st century socioclimatic exposure Proc Natl Acad Sci U S A 104 20195 ndash20198

Diffenbaugh N S J S Pal F Giorgi and X Gao (2007b) Heat stress intensification in the Mediterranean climate change hotspot Geophys Res Lett 34 L11706 doi1010292007GL030000

Giorgi F (2006) Climate change hot-spots Geophys Res Lett 33 L08707 doi1010292006GL025734

Giorgi F (2004) Mean interannual variability and trends in a regional climate change experiment over Europe II Climate change scenarios (2071ndash2100) Clim Dyn 23 839 ndash858

Intergovernmental Panel on Climate Change (IPCC) (2000) Special Report on Emissions Scenarios edited by N Nakiaeligenoviaelig 570 pp Cambridge Univ Press Cambridge U K

Intergovernmental Panel on Climate Change (IPCC) (2007) Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al Cambridge Univ Press Cambridge U K

Overpeck J T et al (1992) Mapping eastern North American vegetation change of the past 18 ka No-analogs and the future Geology 20 1071ndash1074

Pal J S et al (2007) Regional climate modeling for the developing world The ICTP RegCM3 and RegCNET Bull Am Meteorol Soc 89 1395 ndash1409

Rabe B (2004) Statehouse and Greenhouse Brookings Inst Washington D C

Randall D A et al (2007) Climate models and their evaluation in Climate Change 2007 The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S Solomon et al pp 589 ndash662 Cambridge Univ Press Cambridge U K

Trapp R J et al (2007) Changes in severe thunderstorm environment frequency during the 21st century caused by anthropogenically enhanced global radiative forcing Proc Natl Acad Sci U S A 104 19719 ndash 19723

White M A et al (2006) Extreme heat reduces and shifts United States premium wine production in the 21st century Proc Natl Acad Sci U S A 103 11217 ndash11222

Williams J W et al (2007) Projected distributions of novel and disappearing climates by 2100AD Proc Natl Acad Sci U S A 104 5738ndash5742

N S Diffenbaugh Purdue Climate Change Research Center Purdue University 550 Stadium Mall Drive West Lafayette IN 47907-2051 USA (diffenbaughpurdueedu) F Giorgi Abdus Salam International Centre for Theoretical Physics

Strada Costiera 11 I-34014 Trieste Italy J S Pal Frank R Seaver College of Science and Engineering Loyola

Marymount University 1 LMU Drive MS 8135 Los Angeles CA 90045 USA

5 of 5